Ionics最新文献

MnPO₄∙H₂O is an ideal precursor for the preparation of LiMnPO₄. However, the instability of Mn³⁺ in an aqueous solution necessitates the implementation of the existing preparation methods, which are carried out in ethanol and produce toxic gases such as NO and NO₂. In this work, a radical-oxidation coupled phosphate stabilization strategy is proposed for synthesizing MnPO₄∙H₂O in an aqueous solution. The strategy involves the initial oxidation of Mn²⁺ to Mn³⁺ by sulfate radicals, which are produced through the thermal activation of Na₂S₂O₈. Subsequently, Mn³⁺ is stabilized by H₃PO₄, leading to the formation of MnPO₄∙H₂O. By implementing this strategy, the mesoporous MnPO₄∙H₂O precursor can be readily obtained through a reaction at 90 °C for 5 h. Subsequently, the prepared LiMnPO₄/C inherits the mesoporous structure of the MnPO₄∙H₂O precursor, exhibiting excellent electrochemical performance. Specifically, the mesoporous LiMnPO₄/C delivers an initial capacity of 115.8 mAh g⁻¹ with a capacity retention of 83.1% after 100 cycles at 10 C. The enhanced performance is mainly attributed to the mesoporous structure, which facilitates electrolyte penetration, reduces interfacial charge transfer impedance, and accelerates Li⁺ diffusion. The environmentally benign and scalable strategy presented here opens a new approach to the synthesis of high-performance LiMnPO₄/C.

Proper modeling, control and optimization of Proton Exchange Membrane Fuel Cells (PEMFCs) depends on the correct extraction of parameters. This is a nonlinear identification problem that is complex and involves the estimation of seven interdependent parameters using empirical voltage-current data. The paper suggests a new metaheuristic approach, the Starfish Optimization Algorithm (SFOA) based on the regenerative and coordinated feeding behaviors of starfish. The algorithm is strictly tested by reducing the sum-square error (SSE) of the model predictions and experimental data of twelve different PEMFC stacks, whose power was 12 W to 6 kW. The effectiveness of SFOA is proved by the comparative analysis with nine state-of-the-art algorithms (GJO, COA, RSA, PO, SO, YDSE, AOA, RIME, DE). The results consistently indicate that SFOA has the most accuracy with the smallest mean error of 0.0255 being the lowest, the smallest SSE as well as being very robust with a small standard deviation of 1.69E-05. Moreover, SFOA is computationally highly efficient and can reach the optimal solution in 0.38 s, much faster than any of the benchmarked algorithms. The reliability and accuracy of SFOA are validated statistically through convergence curves, box plot analysis and Friedman ranking tests. This study has made SFOA a powerful new instrument in improving the accuracy of fuel cell models, which is essential to create real-time control schemes, system design optimization, and critical monitoring processes.

Adequate energy storage and transformation are crucial for supporting a stable society. Supercapattery devices offer a viable approach to bridging the gap between conventional batteries and supercapacitors (SCs). Metal-organic frameworks (MOFs) are durable networks with interconnected pores formed by metal ions and organic linkers. MXenes are 2-D transition metal carbides or nitrides with high conductivity and reactivity. This study explores the synergy of MOFs, MXenes, and graphene quantum dots (GQDs) doping as advanced electrode materials. In a three-electrode design, the ZIF-90/Nb₂C@GQD electrode recorded 1391 C g⁻¹ in specific capacity when tested at 1 A g⁻¹. A supercapattery device (ZIF-90/Nb₂C@GQD//AC) was fabricated using ZIF-90/Nb₂C@GQD as the anode (positive electrode) and activated carbon (AC) as the cathode (negative electrode). The device sustained an energy density of 70 Wh kg⁻¹ at a power output of 900 W kg⁻¹ and preserved 83.7% of its capacity after 12,000 charge–discharge cycles. The extracted b-values (0.54–0.70) from power-law analysis confirm the hybrid nature of the charge-storage process. The ZIF-90/Nb₂C@GQD composite showed strong hydrogen evolution reaction (HER) performance, with a low Tafel slope of 45.72 mV dec⁻¹. The slight Tafel slope demonstrates the rapid kinetics of the electrocatalytic process. The ZIF-90/Nb₂C@GQD composites demonstrate strong potential for advanced energy storage systems and catalytic energy conversion.

As a possible electrocatalyst, a two-dimensional heterostructure made up of a metal that conducts electricity very well and a metal oxide that conducts electricity has been discovered. In this investigation, we utilized co-precipitation strategy as a simple method to synthesize novel two-dimensional nanosheets of cobalt-doped zinc oxide (Co@ZnO). Through a post-annealing process, the cobalt in the hybrid material is stabilized in the + 2 oxidation state, facilitating the transformation of the precursor into crystalline cobalt-substituted zinc oxide nanosheets while preserving the original morphology. Structural and morphological analyses which revealed the characteristics of the synthesized nanosheets. The electrochemical experiments showed that Co doped ZnO (Co@ZnO) nanosheets, with an optimal doping concentration of 2 wt%, demonstrated outstanding electrocatalytic performance for the ORR in a alkaline medium (0.1 mM KOH), exhibiting an onset potential of 0.88 V and a nearly four-electron mechanism. The Co@ZnO nanosheets displayed superior electrochemical stability compared to pristine ZnO for the ORR. Compared to commercially available RuO₂ catalysts, the Co@ZnO catalyst exhibited significant activity in the OER throughout the experiment. It’s likely that the defect sites and porous structure of Co@ZnO nanosheets worked together to make the catalyst so effective in both OER and ORR.

Conventional proton-exchange membranes (PEMs) like Nafion face conductivity limitations (~ 0.1 S·cm⁻¹). This study explores thermal compression of vermiculite membranes (VMs) as a potential alternative. Controlled thermal compression at 300–500 °C reduced interlayer spacing and improved nanosheet alignment, which make nanochannel dimensions approaching the critical scale for ballistic transport. Consequently, proton conductivity is significantly enhanced. The membrane treated at 400 ℃ achieved a proton conductivity of 1.77 S·cm⁻¹ at 90 °C—far exceeding Nafion under identical conditions. However, temperatures at 500 °C appeared to induce crystallization, increasing proton transport barriers. This suggests that there is a trade-off between the interlayer spacing and the crystallinity. These findings indicate that thermally engineered 2D membranes could offer new pathways for high-conductivity PEM design.

Appropriate operating conditions are essential to ensure the stable performance of proton exchange membrane fuel cells (PEMFCs). In this study, the coupled influence of temperature, pressure, and humidity on the performance and stability of a 3-cells stack were investigated through testing under different conditions at a constant current density of 1600 mA/cm². Although increasing temperature accelerates electrochemical reactions, it exacerbates membrane dehydration, increasing internal resistance and reducing performance. For instance, under low-pressure and low-humidity conditions, the voltage drops from 0.540 V to 0.472 V (a 12.6% decrease) as temperature rises from 75 to 84℃. Increasing pressure not only improves cell performance but also enhances stack consistency while mitigating the impacts of temperature and humidity. Under high-temperature and low-humidity conditions, the voltage increases 37.9%, i.e., from 0.472 V(@70/60 kPa) to 0.651 V (@170/150 kPa). Increasing humidity notably improves performance under low-pressure conditions, but its effect diminishes under high pressure due to the reduced proportion of water vapor partial pressure in the total gas pressure. So, for long-term stable operation of the stack under high-temperature conditions, priority should be given to increasing operating pressure, followed by dynamic adjustment of humidity levels.

This review provides an in-depth exploration of recent advancements in lithium-ion battery (LIB) technology, specifically focusing on graphene-based anode materials and lithium iron phosphate (LiFePO₄) cathodes. The transition from conventional graphite anodes to graphene is emphasized, highlighting its theoretical specific capacity of 744 mAh g⁻¹, which is nearly double that of graphite. The review critically examines the challenges associated with graphene’s use, such as its tendency to aggregate and issues related to its volumetric expansion and low electron mobility. Strategies to address these challenges, including chemical modifications and hybrid material formations, are discussed in detail. The role of LiFePO₄ cathodes in enhancing energy density and ensuring safety, especially for electric vehicle applications, is also explored. Furthermore, modifications to electrolytes, including the incorporation of solid-state and gel electrolytes, are presented as solutions to improve the thermal stability and safety of LIBs. The review also investigates the future prospects of next-generation batteries, such as lithium-sulfur (Li-S) and lithium-air batteries, which offer potential for significantly higher energy densities. Overall, the review underscores the critical advancements and challenges that need to be addressed to enhance the efficiency, safety, and sustainability of LIBs, with a special focus on the electrolyte modifications that are key to achieving these goals.

Accurate estimation of battery state of health (SOH) is essential for prolonging battery lifespan and optimizing battery management systems. This study proposes a hybrid SOH estimation method that integrates data-driven models with equivalent circuit models. Health-related features are extracted from charging-phase measurements and equivalent circuit parameters, and their importance is assessed using the random forest algorithm. Meanwhile, the conventional long short-term memory (LSTM) network is enhanced by incorporating quantum neuron structures and a memory augmentation mechanism. Furthermore, the Kepler Optimization Algorithm (KOA) is employed to automatically tune the key hyperparameters of the proposed QLSTM model, resulting in the KOA-QLSTM framework for battery SOH prediction. Validation using both public datasets and real-world aging data demonstrates that the proposed KOA-QLSTM model achieves excellent predictive performance, with an average root mean square error (RMSE) of less than 1%, indicating high accuracy and robustness.

Accurately estimating the state of health (SOH) of lithium-ion batteries is crucial for ensuring safety, improving economic efficiency, and optimizing battery system operation. However, existing data-driven SOH prediction methods rely on expert experience to extract artificial features. Meanwhile, unsupervised learning-based SOH prediction methods are difficult to capture the long-term dependence of capacity degradation and are prone to overfitting. To address these issues, this paper proposes a novel SOH estimation method based on automatic feature extraction and BiLSTM-SA. In this method, firstly, a Squeeze-and-Excitation block is added to the Temporal Convolutional Network to dynamically adjust the channel weights, and combined with the multi-scale technique to enhance the extraction of battery features, so as to construct an Improved Temporal Convolutional Network (ITCN). Subsequently, the ITCN is fused with an Autoencoder (AE) to form the ITCN-AE structure, and the representative features can be automatically extracted by directly taking the data after wavelet denoising and truncation-alignment operations as the input to the ITCN-AE. Next, the extracted features are fed into a Bidirectional Long Short-Term Memory Network (BiLSTM) to further mine the features, and mapped to the battery health state using a fully connected layer after assigning feature weights through the self-attention (SA) mechanism. Finally, on both the NASA and Oxford lithium-ion battery datasets, the proposed model achieves an average estimation error within 1%. Compared with transformer-based method and the latest LSTM variants, the error is reduced by at least 25.2%. 

This study explores the aggregation properties and interfacial behavior of imidazolium-based ionic liquids [C₁₄mim][Br] and [C₁₅mim][Br], in the presence of the dipeptide glycyl-L-alanine at varying concentrations (0.20, 0.40, 0.60 mol/kg) in aqueous media. Conductivity and surface tension measurements were conducted to determine the critical micelle concentration (CMC) and associated interfacial parameters. The results indicate that the addition of the dipeptide significantly reduces the CMC of both ILs, suggesting enhanced micellization. This behavior is attributed to electrostatic interactions and ion-pair formation between IL head groups and dipeptide molecules. The CMC of ionic liquids increases with temperature. This is because of weakening or disruption of hydrogen bonding between ionic liquid head groups and dipeptide molecule, which diminishes the stabilizing interactions, required for aggregation, thereby hindering micellization. Thermodynamic analysis confirms that micellization is a spontaneous and exothermic process. The Gibbs free energy of micellization (ΔG°_m) becomes increasingly negative with higher dipeptide concentrations, further supporting the enhancement of micellization. Furthermore, surface excess concentration (Γ_max) values were found to be positive, demonstrating reduced surface tension upon IL adsorption. Simultaneously, minimum area per molecule (A_min) increased, suggesting looser molecular packing at the interface. The more negative Gibbs free energy of adsorption (ΔG°_ad) compared to ΔG°_m suggests that adsorption at the air–water interface is thermodynamically favored over micellization. This study provides a systematic evaluation of the influence of a dipeptide on the micellization thermodynamics of imidazolium-based ILs, offering valuable insights into peptide–surfactant interactions in aqueous systems.

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